This　paper　addresses　a　resource-allocation　problem　extracted　from　real　life　application　involving　multi-resource　operations.　A　new　mixed　integer　linear　programming　model　is　proposed　to　minimize　the　weighted　completion　time　while　considering　resource-related　precedence　relationships.　Then,　a　hybrid　algorithm　combining　Benders　decomposition　and　Tabu　search　is　developed　based　on　Benders　decomposition　as　the　basic　framework.　This　method　divides　the　original　problem　into　a　master　problem　for　resource　allocation　and　a　subproblem　of　calculating　the　completion　time　of　each　operation.　The　convergence　is　sped　up　by　improving　the　mathematical　model　and　embedding　the　Tabu　search　approach.　The　experimental　results　on　300　randomly　generated　instances　show　that　when　solving　small-scale　problems,　the　proposed　hybrid　algorithm　can　yield　satisfactory　solutions　with　an　average　deviation　of　0.86　%　from　optimal　ones　provided　by　the　commercial　CPLEX　solver;　when　solving　large-scale　problems,　the　proposed　algorithm　outperforms　the　CPLEX　solver,　the　pure　Tabu　search　algorithm,　the　variable　neighborhood　search　algorithm　and　the　Benders　decomposition　with　embedded　genetic　algorithm.　Compared　with　the　CPLEX,　the　upper　bound　and　lower　bound　are　improved　by　4.74%　and　9.62%　respectively.　©　2024　Northeast　University.　All　rights　reserved.